About ConnectomeDB
Understanding how ligand-receptor (LR) pairs mediate cell-cell communication is essential for advancing both biological and medical research. ConnectomeDB, launched in 2015, is an ongoing project designed to create a comprehensive and highly-accurate database of interacting LR pairs and analysis tools to advance the understanding of cell-cell communication in human and other species. Developers: Perkins Systems Biology & Genomics Lab & YCU Bioinformatics Lab.
If you are using ConnectomeDB or our related tools, please cite our work.
ConnectomeDB2025 - Database & Online Resource for Cell-to-Cell Communication Predictions
- 5,000+ manually curated human LR pairs: most comprehensive and most accurate with primary literature support
- 2,500+ ligands and 2,500+ receptors: extensive gene metadata & annotations
- Biologically relevant content: ontologies, cancers and other diseases
- Homologs in vertebrate species: ~93% LR pairs in mouse and in other species
Publication: ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction (Journal Title, 2025) (TBA)
GitHub repo: https://github.com/bioinfo-YCU/ConnectomeDB (Currently open to developers only)
Using ConnectomeDB2025
The online implementation of ConnectomeDB2025 is a free, no-code, and user-friendly resource. Only a web browser and an internet connection are needed. Please read our Terms & Conditions and start exploring ligand-receptor communication in human, mouse, or other available species by accessing ConnectomeDB 2025.
Not familiar with ConnectomeDB 2025 or want to learn more? Please check out our tutorials.
Your Feedback
We welcome your feedback and suggestions for improving ConnectomeDB content and would be glad to hear any ideas for further development.
If you would like to contribute to the ConnectomeDB project, please visit the GitHub repository (Currently open to developpers only) or email us.
NATMI (Network Analysis Toolkit for Multicellular Interactions)
NATMI is our Python-based toolkit designed for constructing and analyzing multi-cellular communication networks in multiple species. NATMI can use both single-cell and bulk gene expression, as well as proteomic data, to predict cell-to-cell communication at the levels of niches, tissues, and the entire organism.
Publication: Predicting cell-to-cell communication networks using NATMI (Nat commun, 2020)
GitHub repo: https://github.com/forrest-lab/NATMI